The present invention relates to intra-oral methods and apparatus for optically imaging a structure and creating representative 3D models for the structure.
The dental and orthodontic field is one exemplary application of digital generation of 3D models of structures. In many dental applications, a working model of a patient's teeth is needed that faithfully reproduces the patient's teeth and other dental structures, including the jaw structure. Conventionally, a three-dimensional negative model of the teeth and other dental structures is created during an impression-taking session where one or more U-shaped trays are filled with a dental impression material. The impression tray containing the impression material, in its pliant state, is introduced into the mouth of the patient. While the tray and impression material is held in place, the material cures, and after curing, the tray and material are removed from the mouth as a unit. The impression material is allowed to solidify and form an elastic composition, which is the negative mold after removal. The working model is obtained by filling this impression with a modeling material such as dental stone in its liquid state. After being poured into the impression, the dental stone sets and hardens into a solid form which when removed from the impression is a positive representation of the structure of the patient's teeth and tissue in the jaw.
Dental patients typically experience discomfort when the dentist takes an impression of the patient's teeth. The procedure can be even more uncomfortable for the patient if the impression materials run, slump or are otherwise expelled into the patient's throat. Also, shipment and storage of the models can be costly. Hence, determinations of the surface contour of teeth by non-contact optical methods and generation of digital 3D teeth models have become increasingly important.
A basic measurement principle behind 3D optical methods is triangulation. Triangulation techniques are based on known geometric techniques. Given a triangle with the baseline of the triangle composed of two optical centers and the vertex of the triangle the target, the range from the target to the optical centers can be determined based on the optical center separation and the angle from the optical centers to the target.
Triangulation methods can be divided into passive and active. Passive triangulation (also known as stereo analysis) typically utilizes ambient light and both optical centers are typically camera imagers. Active triangulation uses only a single camera imager and, in place of the other camera, uses a source of controlled illumination (also known as structured light). Stereo analysis while conceptually simple is not widely used because of the difficulty in obtaining correspondence of object surface features between camera images. Objects with well-defined edges and corners, such as blocks, may be rather easy to obtain surface feature correspondence, but objects with smoothly varying surfaces, such as skin or tooth surfaces, with no easily identifiable surface features or points to key on, present a significant challenge for the stereo analysis approach.
To overcome the correspondence issue, active triangulation, or structured light, methods project known patterns of light onto an object to infer its shape. The simplest structured light pattern is a spot, typically produced by a laser. The geometry of the setup enables the calculation by simple trigonometry of the active triangulation sensor's range from the scanned object's surface on which the light spot falls. This computed active triangulation sensor's range to the surface of the scanned object will be referred to herein as the surface range data. Typically a sequence of images is gathered with the spot of light moved to fall across different areas of the scanned object's surface and by keeping track of where the active triangulation sensor is positioned with respect to a coordinate reference frame that is fixed with respect to the object being scanned, the sequence of active sensor surface range data can be used to construct a 3D model of the object's surface. Other patterns such as a stripe, or 2-dimensional patterns such as a grid of dots can be used to decrease the required time to capture the set of active triangulation images needed to compute the surface range data for the scanned object's surface of interest.
For active triangulation methods using structured light, such as a single dot or line of light, one source of error arises from any uncertainty in the movement of the object with respect to the active sensor's camera imager while a sequence of image capture steps is underway. Such uncertainty in movement results in uncertainty at each image capture step in the relative position of the camera imager with respect to the surface of the object. This uncertainty in relative position in turn results in errors in the surface range data and the object's modeled 3D surface contour constructed from the range data. Typically, these errors are minimized by mounting the object to be scanned on a reference platform and then moving and positioning the scanner with respect to a coordinate reference frame that is fixed with respect to the reference platform. Since the object being scanned is fixed on the reference platform, the scanner's position with respect to the object is known at each step in the image capture sequence. Alternately, the scanner may be fixed on a reference platform and the object being scanned is then moved and positioned with respect to a coordinate reference frame that is fixed with respect to the reference plate. In either case, the relative position between the scanner and the object, at each image capture step of the scan, can be determined to within the tolerance of the positioning mechanism.
While this method of fixing the object (or scanner) on a platform and then moving the scanner (or object) with a mechanism that links the movement back to the coordinate reference frame of the platform can be effective, there are situations where it is difficult or impractical to fix the relative position between an object to be scanned and the scanner. For example, the intra-oral scanning of dentition can involve uncontrolled movements of the patient's teeth during a scan, which results in an uncertainty of the position of the scanner with respect to the dentition and consequential errors in the 3D models of the intra-oral structures constructed from the captured images.